中文摘要：

针对一类具有未建模误差和扰动的非线性系统的状态估计问题,提出一种在线估计并补偿模型误差的非线性滤波算法,该算法利用非线性预测滤波（NPF）基于预测输出残差的方差最小的基本原则估计模型误差,再利用扩展卡尔曼滤波（EKF）的思想对补偿后的模型进行状态估计;详细推导了状态估计误差及其方差阵的传播模型。以卫星姿态确定系统为例,仿真结果显示改进算法对未建模误差以及初始状态误差的鲁棒性,而滤波周期增大会导致估计精度下降。与扩展卡尔曼滤波和预测滤波相比,改进算法不仅可以实时估计并补偿系统模型误差的影响,提高姿态估计的精度,而且收敛速度快,从而进一步验证了算法的有效性。

英文摘要：

A nonlinear filtering algorithm with online model error estimation and compensation is proposed for a nonlinear system with uncertain model errors and disturbances.The model error is estimated by minimizing the variance of the predictive output residual based on the nonlinear predictive filter（NPF） principle,and then the system state is estimated by the extended Kalman filter（EKF） principle based on the compensated model.Detailed propagation formula of the state estimation error and its variance matrix are derived.Simulation results show the effectiveness of the proposed algorithm on a satellite attitude determination system with uncertain model errors.The simulation shows the robustness of the proposed algorithm for both the initial state error and uncertain model error,and the increasing filtering period may decrease the estimation accuracy.Compared with EKF and NPF,the proposed nonlinear filtering algorithm has the advantage of both improved estimation accuracy and faster convergence.